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Co-occurrence Background Model with Superpixels for Robust Background Initialization
Background initialization is an important step in many high-level applications of video processing, ranging from video surveillance to video inpainting. However, this process is often affected by practical challenges such as illumination changes, background motion, camera jitter and intermittent movement, etc. In this paper, we develop a co-occurrence background model with superpixel segmentation for robust background initialization.
Improvements and Experiments of a Compact Statistical Background Model
Change detection plays an important role in most video-based applications.
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